Time Series Forecasting with Neural Networks
Automatic time series modelling with neural networks.
Allows fully automatic, semi-manual or fully manual specification of networks. For details of the
specification methodology see: (i) Crone and Kourentzes (2010) <10.1016>;
and (ii) Kourentzes et al. (2014) <10.1016>.10.1016>10.1016>
Time series modelling with neural networks for R: nnfor package
Development repository for the nnfor package for R.
Stable version available on CRAN.
To install the development version use:
Otherwise, install the stable version from CRAN:
You can find a tutorial on using nnfor for time series forecasting here.
Nikolaos Kourentzes - (http://nikolaos.kourentzes.com/)
- For an introduction to neural networks see for time series forecasting see: Ord K., Fildes R., Kourentzes N. (2017) Principles of Business Forecasting 2e. Wessex Press Publishing Co., Chapter 10.
- For ensemble combination operators see: Kourentzes N., Barrow B.K., Crone S.F. (2014) Neural network ensemble operators for time series forecasting. Expert Systems with Applications, 41(9), 4235-4244.
- For variable selection see: Crone S.F., Kourentzes N. (2010) Feature selection for time series prediction – A combined filter and wrapper approach for neural networks. Neurocomputing, 73(10), 1923-1936.
This project is licensed under the GPL3 License
CHANGES IN NNFOR VERSION 0.9.6 (15 JAN 2019)
CHANGES IN NNFOR VERSION 0.9.5 (12 JAN 2019)
- Removed functions mseastest, cmav and lagmatrix from nnfor. These are now available in package tsutils.
- Updated license to GPL-3.
CHANGES IN NNFOR VERSION 0.9.4 (11 JUL 2018)
- Fix for msts time series and allow.det.season argument.
CHANGES IN NNFOR VERSION 0.9.3 (01 Apr 2018)
- Changes in how trigonometric seasonal dummies are handled.
CHANGES IN NNFOR VERSION 0.9.2 (11 Dec 2017)
CHANGES IN NNFOR VERSION 0.9.2 (30 Nov 2017)
- Added option to use previously specified mlp and elm (see arguments model and retrain).
CHANGES IN NNFOR VERSION 0.9.1 (29 Oct 2017)
- Fix on univariate keep vector when no univariate lags are selected (lags=0).